PI-D CONTROLLER BASED ON AN IMPROVED CROW SEARCH ALGORITHM FOR CANCER GROWTH TREATMENT

The number of cancer diagnoses and deaths worldwide is rising every year despite technological advancements in diagnosing and treating multiple forms of cancer. An oncolytic virus is a type of tumor-killing virus that can infect and analyze cancer cells while mostly preserving normal cells. The oncolytic Vesicular-Stomatitis Virus therapeutic's cell cycle-specific action mathematically investigated. An optimal Proportion Integral-Derivative (PI-D) controller is introduced in this paper based on a suggested Improved Crow Search Algorithm (ICSA) to enhance the outcome of oncolytic virotherapy. The control technique was tested in a computer using MATLAB simulation. The suggested ICSA is used to tune the parameters of the PI-D controller. The ICSA used the inertia factor and boundary handle mechanism in the position update equation to balance exploration and exploitation. The simulation results show that decrease in total dose, tumor cells to 30%, the tumor remain in the treatment area from day 30 onwards. Furthermore, the ICSA algorithm outperforms the CSA and PSO algorithms by 34.5497×10-6 and 15.2573 ×10-6, respectively, indicating the robustness of treatment methods that can accomplish tumor reduction through biological parameters ambiguity.